Poster — Thur Eve — 72: Conversion of helical tomotherapy plans into clinically favourable step‐and‐shoot IMRT plans deliverable on a c‐arm linac
Bibliographic record
Abstract
The treatment planning software SharePlan is designed to convert dose distributions generated by the TomoTherapy planning station into step-and-shoot IMRT plans deliverable on a c-arm linear accelerator. Five anal canal patients who were planned for TomoTherapy treatments were exported into a SharePlan system and plans were generated for delivery on an Elekta Synergy unit. A total of 80 plans were generated for those five patients, with either seven, nine, eleven or twenty-one gantry angles and different priorities between focusing on matching either the target doses or healthy tissue sparing of the TomoTherapy plan. The plans generated by SharePlan, while often not matching target coverage at prescription, matched well the TomoTherapy coverage at 95% and 105% of the prescription dose. Organ at risk dose, when heavily emphazied in the SharePlan calculations matched or bettered the TomoTherapy dose due to the placement of the beams and the sharper sup-inf fall off of the dose distribution on a linac. For one of the patients, it was possible to produce a better DVH with SharePlan than the original TomoTherapy plan for those reasons. The TomoTherapy plans boasted significantly shorter delivery times than the plans generated with SharePlan.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.046 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".